Predictive User Account Measure for Content Quality and Popularity
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Solution Overview
Problem
Existing online platforms struggle to effectively filter and prioritize content items based on the quality and popularity of user accounts, leading to the dissemination of low-quality content and inefficient resource utilization.
Innovation Solution
Training a machine learning model to generate predicted user account measures that reflect both popularity and quality, using training instances with labeled quality and popularity measures, and employing these measures to restrict the provisioning of content items from low-quality and low-popularity accounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content items are freely published and accessed from all user accounts, then content availability and user engagement are improved, but low-quality and harmful content spreads, consuming computational and network resources
Solution Approach 1:
The system performs preliminary evaluation of user accounts by generating quality measures and popularity measures before content is published or accessed. This advance assessment allows the system to pre-identify high-quality accounts and their content, enabling proactive resource allocation and preventing low-quality content from consuming system resources in the first place.
Solution Approach 2:
The patent introduces an intermediary evaluation mechanism that assesses user accounts and their content before it enters the main content distribution system. This intermediary layer (the quality and popularity measurement system) acts as a filter and gatekeeper, allowing only content from evaluated high-quality accounts to proceed, thereby protecting computational resources from harmful content.
2Adaptability or versatility
If all content items are processed and rendered, then content diversity and user choice are improved, but computational resources are wasted on low-quality content
Solution Approach 1:
The system evaluates user accounts and generates quality measures and popularity measures in advance, before content processing begins. This preliminary assessment enables the system to prioritize computational resources toward content from high-quality accounts while maintaining diversity through the inclusion of multiple evaluated accounts, rather than processing all content uniformly.
Solution Approach 2:
The patent applies different processing standards and resource allocation to different user accounts based on their evaluated quality and popularity. High-quality accounts receive full processing and rendering, while low-quality accounts are filtered out. This local differentiation ensures that computational resources are efficiently allocated to content that warrants full processing, maintaining content diversity among quality content while avoiding waste on low-quality content.
3Object-affected harmful factors
If content filtering is implemented, then harmful content is reduced, but system complexity increases
Solution Approach 1:
The patent introduces intermediary evaluation components that generate quality measures and popularity measures as intermediate representations of user account characteristics. These intermediaries simplify the filtering process by reducing complex content evaluation to measurable metrics, making the harmful content filtering mechanism more manageable and less complex than direct content analysis would require.
Solution Approach 2:
The system transforms the complex task of content quality assessment into parameter-based filtering by converting user account characteristics into measurable quality measures and popularity measures. This parameter transformation simplifies the filtering logic, allowing harmful content to be identified and removed through straightforward threshold comparisons rather than complex analysis algorithms.
4Reliability
If user account measures are generated and stored, then content provisioning control is improved, but data storage requirements increase
Solution Approach 1:
The patent extracts and stores only the essential evaluation metrics (quality measures and popularity measures) rather than storing complete user account data or content. This extraction approach maintains the necessary information for content provisioning control while significantly reducing storage requirements, as only the distilled metrics are retained rather than the full data context.
Data Source
AI summary
Implementations relate to training a model that can be used to process values for defined features, where the values are specific to a user account, to generate a predicted user measure that reflects both popularity and quality of the user account. The model is trained based on losses that are each generated as a function of both a corresponding generated popularity measure and a corresponding generated quality measure of a corresponding training instance. Accordingly, the model can be trained to generate, based on values for a given user account, a single measure that reflects both quality and popularity of the given user account. Implementations are additionally or alternatively directed to utilizing such predicted user measures to restrict provisioning of content items that are from user accounts having respective predicted user measures that fail to satisfy a threshold.